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First observational test of the ‘multiverse’

3 August 2011

The
theory that our universe is contained inside a bubble, and that multiple
alternative universes exist inside their own bubbles – making up the ‘multiverse’
– is, for the first time, being tested by physicists.

Two
research papers published in Physical Review Letters and Physical Review D are
the first to detail how to search for signatures of other universes. Physicists are now searching for disk-like
patterns in the cosmic microwave background (CMB) radiation - relic heat
radiation left over from the Big Bang – which could provide tell-tale evidence
of collisions between other universes and our own.

Many
modern theories of fundamental physics predict that our universe is contained
inside a bubble. In addition to our bubble, this `multiverse’ will contain
others, each of which can be thought of as containing a universe. In the other 'pocket universes' the fundamental constants, and even the basic laws of
nature, might be different.

Until
now, nobody had been able to find a way to efficiently search for signs of
bubble universe collisions - and therefore proof of the multiverse - in the CMB
radiation, as the disc-like patterns in the radiation could be located anywhere
in the sky. Additionally, physicists
needed to be able to test whether any patterns they detected were the result of
collisions or just random patterns in the noisy data.

A team
of cosmologists based at University College London (UCL), Imperial College
London and the Perimeter Institute for Theoretical Physics has now tackled this
problem.

“It’s a very hard statistical and
computational problem to search for all possible radii of the collision imprints
at any possible place in the sky,” says Dr Hiranya Peiris, co-author of the
research from the UCL Department of Physics and Astronomy. “But that’s what pricked
my curiosity.”

The team ran simulations of what the
sky would look like with and without cosmic collisions and developed a ground-breaking
algorithm to determine which fit better with the wealth of CMB data from NASA’s
Wilkinson Microwave Anisotropy Probe (WMAP). They put the first observational
upper limit on how many bubble collision signatures there could be in the CMB
sky.

Stephen
Feeney, a PhD student at UCL who created the powerful computer algorithm to
search for the tell-tale signatures of collisions between "bubble universes",
and co-author of the research papers, said: "The work represents an
opportunity to test a theory that is truly mind-blowing: that we exist within a
vast multiverse, where other universes are constantly popping into
existence."

One of
many dilemmas facing physicists is that humans are very good at cherry-picking
patterns in the data that may just be coincidence. However, the team’s
algorithm is much harder to fool, imposing very strict rules on whether the
data fits a pattern or whether the pattern is down to chance.

Dr
Daniel Mortlock, a co-author from the Department of Physics at Imperial College
London, said: "It's all too easy to over-interpret interesting patterns in
random data (like the 'face on Mars' that, when viewed more closely, turned out
to just a normal mountain), so we took great care to assess how likely it was
that the possible bubble collision signatures we found could have arisen by
chance."

The authors stress that these first
results are not conclusive enough either to rule out the multiverse or to
definitively detect the imprint of a bubble collision. However, WMAP is not the
last word: new data currently coming in from the European Space Agency’s Planck
satellite should help solve the puzzle.

Image caption: The signatures of a bubble collision at
various stages in the analysis pipeline. A collision (top left) induces a
temperature modulation in the CMB temperature map (top right). The 'blob'
associated with the collision is identified by a large needlet response (bottom
left), and the presence of an edge is highlighted by a large response from the
edge detection algorithm (bottom right). In parallel with the edge-detection
step, we perform a Bayesian parameter estimation and model selection analysis.